Estimation of crowding discomfort in public transport: results from Santiago de Chile

The relationship between train occupancy, comfort and perceived security is analysed, using data from a survey and stated choice (SC) study of users of Santiago's Metro (subway) system. Mode choice models where crowding is one of the main explanatory variables are estimated and crowding multipliers to measure its relevance on travel time disutility for sitting and standing are computed. An international comparison with previous studies from London, Paris, Singapore and Sweden is presented. The type of estimated models include Multinomial Logit, Mixed Logit, and Latent Class models. Results show that there is significant heterogeneity in crowding perception across the population. Users classes with low and high crowding multipliers are identified, in which gender, age and income play a role. In the SC survey, occupancy levels were shown with three alternative forms of representation (text, 2D diagram or photo), however we did not find relevant influences of the different forms of representation on crowding perception

Correcting for endogeneity due to omitted crowding in public transport choice using the Multiple Indicator Solution (MIS) method

Crowding levels are very relevant for the analysis and evaluation of the performance of public transport as they strongly affect the level of service and the overall perceived quality of the system. However, crowding is not an easy variable to measure and, hence, demand models often tend to ignore or use abstract proxies for it. In this paper, we assess the Multiple Indicator Solution (MIS) method in a Stated Preference (SP) experiment where crowding conditions were displayed to the respondent but are artificially omitted in the estimation of a curtailed model to cause endogeneity. Results provide evidence that the MIS method can be used to control for a wide range of omitted attributes in SP data. We also discuss the potential application of this approach to Revealed Preferences (RP) models of public transport by asking suitable post-trip questions to users. Two MIS variations were applied to this SP case study and both provided outcomes that were superior to those of the curtailed model. We enrich the analysis with the aid of Monte Carlo simulation. Results suggest that potential problems may arise in the presence of neglected interactions and if indicators are only weakly correlated with the omitted attribute. For the SP case study analysed, only the former issue seems to play a role in the results. The article finishes by discussing the implications of these findings for the correction of endogeneity on SP and RP data on public transport and suggesting future lines of research in this area

Dynamic Model for the Simulation of Equilibrium Status in the Land Use Market

This paper presents a dynamic equilibrium model for the real estate market. Households have stochastic behavior and compete for quasi-unique locations (real estate goods), which are assigned to the best bidder through an auction-type mechanism. The producers are modeled as maximizers of their profits over the long-term through the production of real estate assets, represented by the present value of future sales. It is assumed that the producers do not possess complete information about future levels of demand or prices. Rather, it is assumed that producers are myopic, meaning that they take the actual and historic prices in each period as the relevant information for their decision-making. A notion of equilibrium is used that adjusts prices given two situations: supply and demand surplus. In the supply surplus case, the prices are diminished and supply in the market is reduced until supply equals demand. In the case of demand surplus, the prices rise and demand diminishes (homeless households) until demand equals supply. This equilibrium condition yields prices that are jumpy over time, resembling observations of inventories in the real estate market and the manufacture industry. Copyright Springer Science + Business Media, LLC 2006Dynamic equilibrium, Location, Real estate supply, Residential development,

Evaluating Housing Needs and Preferences of Generation Y in Malaysia

© 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group. In Malaysia, housing providers affect the planning system as housing industry constantly evolves to meet homebuyer needs. Generation Y has exhibited dissimilar housing needs compared to Generation X and Baby Boomer. Thus, housing developers seek to identify the current needs for young homebuyers to avoid experiencing unsold properties. This research aims to identify the fundamental housing needs and psychographic characteristics towards their housing preferences and future planning demands. A quantitative survey was used for collecting data and a statistical analysis was performed to evaluate research outcomes. This research will help local housing developers to understand Generation Y needs and preferences for the future housing demand

About the Categorization of Latent Variables in Hybrid Choice Models

Although hybrid choice models are fairly popular nowadays, the way in which different types of latent variables are considered into the utility function has not been extensively analysed. Latent variables accounting for attitudes resemble socioeconomic characteristics and, therefore, systematic taste variations and categorizations of the latent variables should be considered. Nevertheless, categorizing a latent variable is not an easy subject, as these variables are not observed and consequently exhibit an intrinsic variability. Under these circumstances it is not possibly to assign an individual to a specific group, but only to establish a probability with which an individual should be categorized in given way. In this paper we explore different ways to categorize individuals based on latent characteristics, focusing on the categorization of latent variables. This approach exhibits as main advantage (over latent-classes for instance) a clear interpretation of the function utilized in the categorization process, as well as taking exogenous information into account. Unfortunately, technical issues (associated with the estimation technique via simulation) arise when attempting a direct categorization. We propose an alternative to attempt a direct categorization of latent variables (based on an auxiliary variable) and conduct a theoretical and empirical analysis (two case studies), contrasting this alternative with other approaches (latent variable-latent class approach and latent classes with perceptual indicators approach). Based on this analysis, we conclude that the direct categorization is the superior approach, as it offers a consistent treatment of the error term, in accordance with underlying theories, and a better goodness-of-fit